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Comprehension of spoken natural language is an essential component for robotsto communicate with human effectively. However, handling unconstrained spokeninstructions is challenging due to (1) complex structures including a widevariety of expressions used in spoken language and (2) inherent ambiguity ininterpretation of human instructions. In this paper, we propose the firstcomprehensive system that can handle unconstrained spoken language and is ableto effectively resolve ambiguity in spoken instructions. Specifically, weintegrate deep-learning-based object detection together with natural languageprocessing technologies to handle unconstrained spoken instructions, andpropose a method for robots to resolve instruction ambiguity through dialogue.Through our experiments on both a simulated environment as well as a physicalindustrial robot arm, we demonstrate the ability of our system to understandnatural instructions from human operators effectively, and how higher successrates of the object picking task can be achieved through an interactiveclarification process.

Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions